Empirical Software Engineering

Software Energy Consumption is gaining more and more attention. In this paper, we tackle the problem of warning developers about the increase of SEC of their programs during Continuous Integration (CI).

In this study, we investigate ...

Docker is the de facto standard for software containerization. A Dockerfile contains the requirements to build a Docker image containing a target application. There are several best practice rules for writing Dockerfiles, but the developers do not ...

Modern applications are often developed using a combination of programming languages and technologies. Multi-language systems offer opportunities for code reuse and the possibility to leverage the strengths of multiple programming languages. ...

Code smells (CS) are symptoms of poor design and implementation choices that may lead to increased defect incidence, decreased code comprehension, and longer times to release. Web applications and systems are seldom studied, probably due to ...

Release notes (RNs) refer to the technical documentation that offers users, developers, and other stakeholders comprehensive information about the changes and updates of a new software version. Producing high-quality RNs can be challenging, and it ...

Modern software systems are composed of several loosely coupled components. Typical examples of such systems are plugin-based systems, microservices, and modular software systems. Such types of software systems have several advantages motivating a ...

Software development teams generally welcome any effort to expose bugs in their code base. In this work, we build on the hypothesis that mobile apps from the same category (e.g., two web browser apps) may be affected by similar bugs in their ...

A long continuous integration (CI) build forces developers to wait for CI feedback before starting subsequent development activities, leading to time wasted. In addition to a variety of build scheduling and test selection heuristics studied in the ...

The growing software development market has increased the demand for qualified professionals in Software Engineering (SE). To this end, companies must enhance their Recruitment and Selection (R&S) processes to maintain high-quality teams, ...

Machine learning (ML), including deep learning, has recently gained tremendous popularity in a wide range of applications. However, like traditional software, ML applications are not immune to the bugs that result from programming errors. Explicit ...

With the increasing complexity and scope of software systems, their dependability is crucial. The analysis of log data recorded during system execution can enable engineers to automatically predict failures at run time. Several Machine Learning (...$\mathrm{}$$\mathrm{}$

Despite the accuracy of machine learning (ML) techniques in predicting invalid bug reports, as shown in earlier research, and the importance of early identification of invalid bug reports in software maintenance, the adoption of ML techniques for ...

Previous studies have shown that Automated Program Repair (apr) techniques suffer from the overfitting problem. Overfitting happens when a patch is run and the test suite does not reveal any error, but the patch actually does not fix the ...

Due to the prevalent application of machine learning (ML) techniques and the intrinsic black-box nature of ML models, the need for good explanations that are sufficient and necessary towards locally interpreting a model’s prediction has been well ...

We investigated whether (possibly wrong) security patches suggested by Automated Program Repairs (APR) for real world projects are recognized by human reviewers. We also investigated whether knowing that a patch was produced by an ...

[Context] The continuous inflow of bug reports is a considerable challenge in large development projects. Inspired by contemporary work on mining software repositories, we designed a prototype bug assignment solution based on machine learning in ...

The term smart contract was originally used to describe automated legal contracts. Nowadays, it refers to special programs that run on blockchain platforms and are popular in decentralized applications. In recent years, vulnerabilities in smart ...

Representing the textual semantics of bug reports is a key component of bug report management (BRM) techniques. Existing studies mainly use classical information retrieval-based (IR-based) approaches, such as the vector space model (VSM) to ...

Researchers testing hypotheses related to factors leading to low-quality software often rely on historical data, specifically on details regarding when defects were introduced into a codebase of interest. The prevailing techniques to ...

Psychological safety continues to pique the interest of scholars in a variety of disciplines of study. Recent research indicates that psychological safety fosters knowledge sharing and norm clarity and complements agile values. Although software ...

Title quality is important for different software engineering communities. For example, in Stack Overflow, posts with low-quality question titles often discourage potential answerers. In GitHub, issues with low-quality titles can make it difficult ...

Just-in-Time software defect prediction (JIT-SDP) prevents the introduction of defects into the software by identifying them at commit check-in time. Current software defect prediction approaches rely on manually crafted features such as change ...

Graph Neural Networks (GNNs) have gained prominence in various domains, such as social network analysis, recommendation systems, and drug discovery, due to their ability to model complex relationships in graph-structured data. GNNs can exhibit ...

Empirical studies with human participants (e.g., controlled experiments) are established methods in Software Engineering (SE) research to understand developers’ activities or the pros and cons of a technique, tool, or practice. Various ...

Developers insert logging statements to collect information about the execution of their systems. Along with a logging framework (e.g., Log4j), practitioners can decide which log statement to print or suppress by tagging each log line with a log ...

With an increase of severity in software vulnerabilities, machine learning models are being adopted to combat this threat. Given the possibilities towards usage of such models, research in this area has introduced various approaches. Although ...

The widespread adoption of video-based applications across various fields highlights their importance in modern software systems. However, in comparison to images or text, labelling video test cases for the purpose of assessing system accuracy can ...$$$$

Modern software development is a complex engineering process where developer code cohabits with an increasingly larger number of external open-source components. Even though these components facilitate sharing and reusing code along with other ...

Deep Neural Network (DNN) models are widely used in many cutting-edge domains, such as medical diagnostics and autonomous driving. However, an urgent need to test DNN models thoroughly has increasingly risen. Recent research proposes various ...

Jupyter notebooks have emerged as the predominant tool for data scientists to develop and share machine learning solutions, primarily using Python as the programming language. Despite their widespread adoption, a significant fraction of these ...